Colour information biases facial age estimation and reduces inter-observer variability

doi:10.21203/rs.3.rs-2062498/v1

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Colour information biases facial age estimation and reduces inter-observer variability

https://doi.org/10.21203/rs.3.rs-2062498/v1

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published 14 Aug, 2023

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Age estimation is a perceptual task that people perform automatically and effortlessly on a daily basis. Colour has been identified as one of the facial cues that contributes to age perception. To investigate further the role of colour in age perception, we manipulated the chromatic content of facial images holistically. In Experiment 1, images were shown in colour or greyscale; in Experiment 2, images were shown with red-green contrast increased or decreased; in Experiment 3, images were shown with modified yellow-blue contrast. We examined whether the presence of chromatic information biases the perception of age and/or affects inter-observer variability in age judgements, and whether specific chromatic information affects the perception of age. We found that the same face tended to be judged as younger with increased red-green contrast compared to decreased red-green contrast, suggesting that red-green contrast directly affects age perception. Inter-observer variability in age ratings was significantly lower when participants were asked to rate colour compared with greyscale versions of images. This finding indicates that colour carries information useful cues for age estimation.

Age is an important social attribute, making age estimation an ecologically relevant component of face perception. It determines rights, responsibilities and even social status (Mayes et al., 2010). However, perceived age does not always reflect biological age, since facial appearance is also influenced by environmental and lifestyle factors (Mayes et al., 2010). A longitudinal study of Danish twins reported that perceived age is a better biomarker of health than chronological age due to the influence of lifestyle and social factors (Christensen et al., 2009). This social relevance is also reflected in most people wanting to be perceived as younger, since looking youthful is associated with health and attractiveness (Barber, 1995; Hsieh et al., 2021; Symons, 1995).

The eyes and lips have been reported to be particularly relevant to estimating age compared to other facial features (Gunn et al., 2009; Nkengne et al., 2008). The surrounding area, shape, and size of the eyes and sclera colour have been shown to help people make a judgement of age (Russell et al., 2014). Lips are potentially a reliable cue to age since their volume (Gunn et al., 2009) and redness (Gomi & Imamura, 2019) decrease significantly with age. Other facial features also change during the aging process. For example, nose shape changes significantly with age (Sforza et al., 2011), but it is not yet clear if people use the nose as a cue to estimate age.

Surface-based cues are also associated with perceived age (Bruce & Langton, 1994; Kemp et al., 1996; Russell et al., 2006). Increased sun exposure is associated with higher perceived age and has been described as the principal extrinsic factor (Leyden, 1990; Ortonne, 1990). Chronic exposure to sunlight is thought to stimulate melanogenic activity and create non-uniformity of skin pigmentation (Matts et al., 2007) and wrinkling (Gunn et al., 2009), which are both cues for age estimation. Moreover, face shape produces shading differences that affect the luminance profiles of images of the face (Bruce & Langton, 1994; Hill et al., 1995; Kemp et al., 1996). Empirically, digitally reducing skin wrinkles, skin sagging, or skin colour heterogeneity was shown to give a more youthful appearance (Fink et al., 2006; Matts et al., 2007).

Colour information of the face has not only been reported to be important in the perception of age (Burt & Perrett, 1995; Matts et al., 2007), but also contribute significantly to perceptions of sex and ethnicity (Hill et al., 1995; Tarr et al., 2001), health (Matts et al., 2007; Re et al., 2011; Russell et al., 2014), attractiveness (Re et al., 2011; Stephen & McKeegan, 2010), and identity (Yip & Sinha, 2002). When investigating age perception, Fink et al. (2006) applied skin colour distributions from different ages to identical three-dimensional (3D) shape-standardized female faces. Subjects rated age, health, and beauty of the 3D rendered faces. Faces with younger surface-based information were perceived as younger and received significantly higher ratings for attractiveness and health. Analysis of the images revealed that the skin colour distribution of younger faces was more homogenous compared to older people. Fink et al. (2006) suggested that skin colour heterogeneity provides information independent of facial form and skin surface topography, that contributes to the perception of female facial age and judgements of attractiveness and health.

The contrast between facial features (e.g., eyes and lips) and the surrounding skin has been termed ‘facial contrast’ (Porcheron et al., 2013; Russell, 2003) (note that this is different from ‘skin contrast’ as defined by other researchers (Matts et al., 2007)). Female faces have greater facial contrast than male faces, and facial contrast plays an important role in sex classification and attractiveness (Russell, 2003; Stephen & McKeegan, 2010). Porcheron et al. (2013) proposed that facial contrast decreases with age and serves as a cue for identifying age. They measured the facial contrast of Caucasian women faces within the CIE L*a*b* colour space (Lab space) and asked participants to estimate age from the original and contrast-manipulated images. Female face images were judged to be significantly younger when shown with greater a* (red-green) contrast around the mouth, greater luminance contrast around the eyes, or greater luminance contrast around the eyebrows. Note that these estimates of luminance facial contrast do not differentiate between skin pigmentation and luminance cues related to topographic factors, and these estimates of facial contrast require individual labelling of facial regions of interest, making these measures difficult to standardise. Nonetheless, these findings suggest that colour contrast both within features/skin and between features are potential cues to age.

A key challenge for facial age estimation research is the fact that faces are complex stimuli, and the space of possible variables driving age perception is large. A standard approach is to systematically manipulate a facial feature and obtain age ratings (Fink et al., 2006; Porcheron et al., 2013). When judgements of age systematically change with the feature manipulation, this suggests that the facial feature is relevant to age perception. While this approach is valid, it relies on the researcher first identifying candidate facial features, leaving open the possibility that more complex and/or subtle features are missed. Here, we manipulate colour and contrast of face images holistically rather than arbitrarily manipulating features.

Experiment 1

We tested whether colour rather than greyscale presentation systematically biased the perception of age, or affected inter-observer variability in age judgements. We conducted data cleaning before running any analyses. There were 7800 ratings collected from 39 included participants. From these, we excluded ratings under 13 years since images from the Chicago face dataset are all images of adults. We also excluded absolute deviations from the CFD average rated age larger than 25. By doing so, 32 of 7800 trial ratings were excluded.

To establish if presentation in colour versus greyscale systematically biases ratings of age, we compared the median rating of each image in colour (M = 32.54, SD = 9.47) and greyscale (M = 32.37, SD = 9.64). A paired-sample t-test indicated there was no significant difference between image conditions (t₁₉₉ = 1.37, p = .19; Fig. 1a).

The standard deviation (SD) in ratings gives a measure of inter-observer variability, which reflects the degree of agreement among independent observers, for each image in each of the two conditions. Specifically, the lower the SD, the lower the variability and the higher the agreement. To investigate whether image condition affected inter-observer variability, we compared SDs of the rating of each image in the colour (M = 5.74, SE = 1.61) and greyscale (M = 6.06, SE = 1.96) conditions. A paired-sample t-test revealed that inter-observer variability was significantly higher in the greyscale condition than in the colour condition (t₁₉₉ = -2.65, p = 0.009, Fig. 1b).

Experiment 2

We further investigated if facial age estimation is affected by specific chromatic information, specifically red-green contrast. There was a total of 8200 ratings collected from 41 included participants. Using the same data cleaning criteria as in Experiment 1, we excluded 20 of 8200 ratings. To clarify if presentation in increased versus decreased red-green contrast systematically biases ratings of age, we compared the median rating of each image in the increased (M = 31.99, SD = 9.31) and decreased red-green contrast conditions (M = 32.57, SD = 9.41) with a paired-sample t-test (t₁₉₉ = 4.46, p < .001). There was a significant difference between image conditions such that faces tend to be rated younger when presented in the higher red-green contrast condition. (Fig. 2a).

We also investigated whether image conditions affected inter-observer variability. A comparison of the SD of the rating of each image in both lower red-green contrast (M = 5.78, SE = 1.54) and higher red-green contrast (M = 5.64, SE = 1.42) condition with a paired-sample t-test (t₁₉₉ = 1.36, p = .17) revealed no significant difference for inter-observer variability (Fig. 2b).

Experiment 3

We further investigated if facial age estimation is affected by yellow-blue contrast. There was a total of 8600 ratings collected from 43 included participants. Using the same data cleaning criteria as in Experiment 1, we excluded 57 of 8600 ratings. Comparing the increased (M = 33.18, SD = 9.91) and decreased yellow-blue contrast conditions (M = 33.28, SD = 10.13), the paired-sample t-test on the median rated age (t₁₉₉ = -0.50, p = .62) was not significant (Fig. 3a).

We also investigated whether image conditions affected inter-observer variability. A comparison of the SD of the rating of each image in the lower yellow-blue contrast (M = 6.15, SE = 1.62) and higher yellow-blue contrast (M = 6.18, SE = 1.58) conditions with a paired-sample t-test (t₁₉₉ = -0.27, p = .79) revealed no significant difference for inter-observer variability (Fig. 3b).

The aim of the current study was to establish whether colour and chromatic contrast contribute to facial age judgements. We examined whether colour information biases the perception of age, or affects inter-observer variability in age judgements. We also examined whether specific chromatic information affects the perception of age. When participants were required to rate the colour and greyscale versions of images in Experiment 1, the inter-observer SD of ratings for individual images was found to be significantly lower for colour presentation. While we found no evidence that the simple presence or absence of colour systematically affects our perception of age, this finding indicates that chromatic information provides extra information about age, beyond that carried by the achromatic content of the image, and that this is used when making age judgements, in a way that is broadly consistent across observers. Comparing median ratings within Experiment 2, we found that face images with higher red-green contrast were rated significantly younger. This finding demonstrates that red-green contrast can directly influence the perception of age such that the same face tends to be rated as younger with increased compared to decreased red-green contrast.

Recent studies have suggested that redness is associated with the perception of various facial attributes, such as age, attractiveness, and health. However, it is unclear whether hues (Stephen & McKeegan, 2010) or colour contrasts (Porcheron et al., 2013) are driving the results. Representing face images in Lab space, it is evident that there is a predominance of red (> 0) and yellow (> 0); there is very little green and even less blue (Fig. 4). Across 200 images, 81.64% of pixels had a*>0 and 91.52% of pixels had b*>0. Thus, by manipulating different Lab space contrasts, we were mainly manipulating “redness” and “yellowness” in the facial images. Specifically, increasing red-green contrast increased redness while increasing yellow-blue contrast increased yellowness.

Our finding that increasing red-green contrast (effectively increasing redness) makes faces look younger complements previous research. Previous studies have shown that increasing the redness of particular facial features (e.g., lips, skins) makes faces look younger (Fink et al., 2006; Matts et al., 2007; Ortonne, 1990; Porcheron et al., 2013). While youthfulness is often associated with health and attractiveness, increasing redness can also make faces look healthier (Perrett et al., 2020; Stephen et al., 2009) or more attractive (Fink et al., 2006; Matts et al., 2007; Stephen & McKeegan, 2010). Facial redness is an indicator of the level of blood oxygenation, which may be interpreted by observers as a cue to underlying physiological health or quality (Re et al., 2011; Stephen et al., 2009).

In terms of limitations of the current study, it is important to note that the age of the majority of face stimuli lies between 20 and 40 years old. Hence, we cannot directly apply our findings to ages outside of this demographic group. We also acknowledge that the chromatic information in the images used in the current study does not isolate surface-based cues. Shading created by facial topography may also make a contribution. Even though the facial bones almost stop growing after the age of 20, increasing age leads to a dissipation of the soft tissue in the face resulting in the bony appearance of the face in older adults (Enlow & Moyers, 1982). Nose shape (Sforza et al., 2011) and the thickness of the soft-tissue (Farkas et al., 2004) change significantly with age. Such changes in facial topography may cause differential shading that influences the chromatic content of facial images.

Inter-observer variability in age ratings was found to be significantly greater in the greyscale condition compared to the colour condition. This inter-observer variance reflects the degree of agreement among independent observers, for each image under each of the two conditions. Inspection of the data indicates an ethnicity effect, such that there is a disproportionately high number of black faces among those that have a significantly higher SD in greyscale conditions (Fig. 1b). A post-hoc chi-squared test confirmed that the ethnicities of these images did indeed differ from the expected distribution, X² (3, N = 200) = 8.63, p = .05. This suggests that the perceived age of black faces is more easily influenced by colour compared to other faces, suggesting that there may be more colour information about age in black faces compared to other ethnicities.

In summary, our experiments demonstrate that the mere presence of chromatic information reduces inter-observer variability in age judgements and that manipulating red-green contrast biases the perception of age such that redness tends to be associated with youthfulness. Possible directions for future work are to test whether the red-green contrast effect is driven by a hue shift rather than by red-green contrast, and whether the red-green contrast effect generalises to stimuli of a larger age range.

Participants

A different set of participants was recruited for each of the 3 experiments (Table 1 for demographics). All participants had normal or corrected to normal vision. In each experiment, a minority of participants gave unreliable ratings and their data were excluded. A summary of participant demographics and the stimulus characteristics across all 3 experiments is shown below (Table 1). All participants provided informed consent and ethical approval for this study was granted by the UNSW Human Research ethics committee, all methods were performed in accordance with the Declaration of Helsinki. Participants were all UNSW psychology undergraduate students and were recruited through the UNSW SONA system (Research management system) and received course credit.

Table 1

Summary of participant demographics and stimulus characteristics.
exp	ppts tested	excluded	included (M/F)	image conditions
1	40	1	9/30, with a mean age of 19.05 (SD = 1.52).	colour (original)/greyscale
2	45	4	12/29, with a mean age of 20.59 (SD = 5.88).	higher/lower red-green contrast
3	44	1	3/40, with a mean age of 19.05 (SD = 1.52).	higher/lower yellow-blue contrast

Stimuli

Stimuli were based on 200 full face images from the main Chicago Face Database (CFD) set of models recruited in the United States (Ma et al., 2015). All selected images showed models photographed with neutral facial expressions, in standardised lighting against a white background. There is no ground-truth age information in the main CFD set, but there are subjective age rating norms for each image (based on n = 1,087 ratings). We used these average age ratings to identify outliers in our data, excluding participants where the correlation between their ratings and the mean rated ages from the Chicago Face Database (CFD) set was < 0.5. Subjective age rating norms for the images were not for the analysis proper. By self-reported ethnicity, there are images of 60 asian, 60 white, 40 black, and 40 latino faces. The number of self-identified female and male images was balanced for each ethnicity. To manipulate the colour and contrast of the face, we used CIE Lab space which is a colour space of approximately uniform perceptual discriminability, where distances between coordinates of stimuli are predictive of perceived colour difference between the stimuli (Tkalcic & Tasic, 2003). The 3 orthogonal dimensions of this colour space are light-dark (L*), red-green (a*), and yellow-blue (b*). Here we manipulated contrast along the chromatic dimensions while keeping the achromatic content (L*) constant. Demonstration images for each experiment are shown in Fig. 5. In Experiment 1, images were shown in original colour or greyscale version; in Experiment 2, images were shown with red-green contrast increased or decreased by a factor of 1.5 while images were shown with similarly modified yellow-blue contrast in Experiment 3. For each image, we first transformed the image from sRGB to CIE Lab, performed the contrast manipulation, then transformed the image back from CIE Lab to sRGB space, using the white point of CIE standard illuminant D65. The magnitude of the manipulations in Experiment 2 and 3 was based on informal piloting. The difference is perceptible but the faces still look natural.

Procedure and Design

This was an online study that allowed participants to join from a personal computer running a Google Chrome browser. We used JATOS (Lange et al., 2015) and jsPsych (de Leeuw, 2015) hosted by a server within the UNSW School of Pscyhology. Participants were required to run the experiment in full-screen mode, Image size was calibrated to 18 cm (w) x 25.6 cm (h) by adjusting an on-screen rectangle to the size of a credit card prior to the experiment. Participants were asked to rate the age of faces by entering a two-digit integer (between 10 and 99) on the number pad of a computer keyboard. Participants were not informed about the age range of the stimuli and no feedback was given as to the accuracy of their responses. On each trial, an image was presented for 2 seconds and then removed. A written prompt and a text box would pop up on the screen until the participant made their response. Each participant completed 200 trials with 100 images presented in colour and 100 in greyscale. Each participant rated each face only once, in colour or greyscale. Stimulus presentation was counterbalanced such that, across participants, each stimulus identity was presented the same number of times in colour and greyscale. For each image, there were, across participants, 20 ratings for the colour version and 20 ratings for the greyscale version.

Credit authorship contribution statement

Jean Y. J. Hsieh: Conceptualization, Methodology, Software, Investigation, Formal analysis, Writing – original draft.

W. Paul Boyce: Conceptualization, Methodology, Software, Writing – review & editing.

Erin Goddard: Conceptualization, Methodology, Formal analysis, Writing – review & editing.

Colin W.G. Clifford:Conceptualization, Methodology, Formal analysis, Writing – review & editing, Resources.

Declaration of Competing Interest

None.

Data availability statement

The datasets generated during the current study are available from the corresponding author on reasonable request.

Acknowledgements

This work was supported by Australian Research Council Discovery Project DP190100491 to CC.

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No competing interests reported.

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Colour information biases facial age estimation and reduces inter-observer variability

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